Remote sensing technology in vegetation research: progress and prospects

Vegetation plays a vital role in maintaining ecosystem stability, regulating climate, and providing ecological services. Traditional vegetation survey methods have limitations. They often cover a small range, work at low efficiency, and fail to provide long-term continuous data. With the rapid development of remote sensing technology, the evolution of vegetation monitoring has shifted from coarse-resolution satellite imaging to integrated multi-source, high-resolution, and AI-driven monitoring frameworks, forming a systematic methodology with clear theoretical underpinnings. Recent advances in high-resolution imagery, LiDAR, and deep learning integration have substantially improved monitoring capability, enabling fine-scale species identification and dynamic change detection over large areas. This review synthesizes current trends, forecasts future directions including intelligent multi-source fusion, real-time monitoring via cloud platforms, and predictive modeling for climate-vegetation interactions, which provide important theoretical references for global ecological management.